The Usefulness of the APACHE II Score in Obstetric Critical Care: A Structured Review
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) mortality prediction model in pregnant and recently pregnant women receiving critical care in low-, middle-, and high-income countries during the study period (1985-2015), using a structured literature review. DATA SOURCES: Ovid MEDLINE, Embase, Web of Science, and Evidence-Based Medicine Reviews, searched for articles published between 1985 and 2015. STUDY SELECTION: Twenty-five studies (24 publications), of which two were prospective, were included in the analyses. Ten studies were from high-income countries (HICs), and 15 were from low- and middle-income countries (LMICs). Median study duration and size were six years and 124 women, respectively. DATA SYNTHESIS: ICU admission complicates 0.48% of deliveries, and pregnant and recently pregnant women account for 1.49% of ICU admissions. One quarter were admitted while pregnant, three quarters of these for an obstetric indication and for a median of three days. The median APACHE II score was 10.9, with a median APACHE II-predicted mortality of 16.6%. Observed mortality was 4.6%, and the median standardized mortality ratio was 0.36 (interquartile range 0.23 to 0.73). The standardized mortality ratio was < 0.9 in 24 of 25 studies. Women in HICs were more frequently admitted with a medical comorbidity but were less likely to die than were women in LMICs. CONCLUSION: The APACHE II score consistently overestimates mortality risks for pregnant and recently pregnant women receiving critical care, whether they reside in HICs or LMICs. There is a need for a pregnancy-specific outcome prediction model for these women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".